dc.contributor.authorHo, Shen-Shyang.
dc.date.accessioned2013-10-25T03:39:17Z
dc.date.available2013-10-25T03:39:17Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationHo, S. S. (2012). Preserving privacy for moving objects data mining. 2012 IEEE International Conference on Intelligence and Security Informatics, 135-137.en_US
dc.identifier.urihttp://hdl.handle.net/10220/16905
dc.description.abstractThe prevalence of mobile devices with geopositioning capability has resulted in the rapid growth in the amount of moving object trajectories. These data have been collected and analyzed for both commercial (e.g., recommendation system) and security (e.g. surveillance and monitoring system) purposes. One needs to ensure the privacy of these raw trajectory data and the derived knowledge by not disclosing or releasing them to adversary. In this paper, we propose a practical implementation of a (ε; δ)-differentially private mechanism for moving objects data mining; in particular, we apply it to the frequent location pattern mining algorithm. Experimental results on the real-world GeoLife dataset are used to compare the performance of the (ε; δ)-differential privacy mechanism with the standard ε-differential privacy mechanism.en_US
dc.language.isoenen_US
dc.rights© 2012 IEEE.en_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titlePreserving privacy for moving objects data miningen_US
dc.typeConference Paper
dc.contributor.conferenceIEEE International Conference on Intelligence and Security Informatics (2012 : Arlington, Virginia, US)en_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/ISI.2012.6284198


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